An experimental evaluation of novelty detection methods

Xuemei Ding, Yuhua Li, Ammar Belatreche, LP Maguire

Research output: Contribution to journalArticle

71 Citations (Scopus)

Abstract

Novelty detection is especially important for monitoring safety-critical systems in which novel conditions rarely occur and knowledge about novelty in that system is often limited or unavailable. There are a large number of studies in the area of novelty detection, but there is a lack of a comprehensive experimental evaluation of existing novelty detection methods. This paper aims to fill this void by conducting experimental evaluation of representative novelty detection methods. It presents a state-of-the-art review of novelty detection, with a focus on methods reported in the last few years. In addition, a rigorous comparative evaluation of four widely used methods, representative of different categories of novelty detectors, is carried out using 10 benchmark datasets with different scale, dimensionality and problem complexity. The experimental results demonstrate that the k-NN novelty detection method exhibits competitive overall performance to the other methods in terms of the AUC metric.
Original languageEnglish
Pages (from-to)313-327
JournalNeurocomputing
Volume135
DOIs
Publication statusPublished - 5 Jul 2014

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